Analytics and Learning Methods for Smart Cities

(3 Credits)

Basics of analytics and learning methods, with extensive applications in smart cities. Focuses on introduction of analytics and learning algorithms in their very basic forms, implementation in common coding languages, and smart city applications. Topics include probability review, inference, linear regression, classification, neural networks, and introduction to reinforcement learning. Applications include autonomous vehicles, traffic control, public transit, ridesharing, urban emergency response, smart grid, and smart buildings. | Prerequisites: CS-UY 1113 and MA-UY 2224 , or equivalents.